Direct Visual-Inertial Ego-Motion Estimation Via Iterated Extended Kalman Filter

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number8966307
Pages (from-to)1476-1483
Journal / PublicationIEEE Robotics and Automation Letters
Issue number2
Online published22 Jan 2020
Publication statusPublished - Apr 2020


This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The state prediction makes use of IMU readings while the state update relies directly on photometric feedback as measurements. Unlike feature-based methods, the photometric difference for the innovation term renders an inherent and robust data association process in a single step. The proposed approach is validated using real-world datasets. The results show that the proposed method offers better robustness, accuracy, and efficiency than a feature-based approach. Further investigation suggests that the accuracy of the flight velocity estimates from the proposed approach is comparable to those of two state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is ≈15-30 times faster thanks to the omission of reconstruction and mapping.

Research Area(s)

  • Aerial systems: perception and autonomy, sensor fusion